Modeling healthcare as a complex system - a needed advancement in healthcare research
Background: Conventional healthcare research has long been conducted as linear, static analyses, which is not representative of the complex, real-life scenarios that define healthcare systems. For example, downstream effects of interventions can happen in inter-connected realms of healthcare and possibly over long periods of time but are often not accounted for. Also, analyses are usually conducted on averages over a spectrum of demographics and culture. In real-life, individual demographics are known to play a key role in healthcare studies. Now, with electronic health records, big data has the potential to advance healthcare research significantly. However, there is still a need for research methods that match our availability of data to produce higher quality results in healthcare research. Complex system modeling matches our highest level of understanding of a system with the availability of data in the modern age.
Hypothesis: Complex system modeling integrates our knowledge of the parts of a system together to develop a composite understanding of the overall system that is more accurately reflective of the real world. This type of modeling is applicable in a variety of realms of healthcare and can help to enhance our understanding, even in areas where significant amounts of traditional research has been done.
Methods: I identify four disparate areas in the realm of healthcare, and I compare current work to the complex systems model I generate on each topic. Specifically, I present the following four case studies:
- The system of female under-representation in academic medicine (Qualitative)
- The dynamics of oxygenation of the body and various disease states (Quantitative)
- Patient flow through an emergency department (Quantitative)
- Poison control center integration with a health information exchange (Quantitative)
With each, I will discuss the robust methods in generation of the models, and then highlight gaps of knowledge that conventional research methods are unable to ascertain. I will do so in a visual and dynamic manner that is both informational and accessible.
Conclusion: Complex system modeling is extremely powerful as it capitalizes on the non-linearity and dynamic nature of systems and appropriately consumes information derived from big data. This power has been demonstrated in these simulations presented, representing a variety of aspects of healthcare, including in social sciences of academic medicine, hospital operations, clinical research, and non-clinical research. Through complex systems modeling, higher fidelity simulations can be created than ever before. Integrated with big data, these simulations are powerful tools for predictive modeling as they more accurately reflect the nature of real-world systems.